Abstract The prevalence of multidrug-resistance in Gram-negative bacteria (e.g., Pseudomonas aeruginosa, Acinetobacter baumannii), is rising at an alarming rate, rendering many (if not all) antibiotics ineffective when used alone. The rate of new drug development is unlikely to keep pace with the increase in multidrug resistance. Combination therapy is often used clinically as a last resort. However, considering the numerous possibilities, combination therapy are selected by clinicians mostly based on anecdotal experience and intuition. A robust method to guide rational selection of combination therapy would be crucial to delay returning to the pre-antibiotic era. Our long-term goal is to optimize clinical use of antibiotics to combat the emergence of resistance. The objective of this application is to develop a novel precision medicine platform (monitoring device and data processing algorithm) that will guide the design of combination therapy. If short-term experimental data can be used to predict the response of patient-specific bacteria to clinically relevant antibiotic exposures, effective treatment strategies could be formulated rationally by identifying the best possible combination, thus guiding clinicians in the selection of combination therapy. We plan to accomplish the objective of the application as follows: (1) enhance testing throughput for optimal clinical application; (2) identify useful antibiotic combinations against multidrug resistant bacteria; and (3) maximize the potentiating effect of agents targeting specific mechanisms of resistance (e.g., ?-lactamase inhibitors) in drug-resistant bacteria. In this application, the proposed approach will be illustrated by experimental data with P. aeruginosa, A. baumannii and Klebsiella pneumoniae. However, the proposed model-based system is not confined to a specific antimicrobial agent - pathogen combination. It could be extrapolated to other antimicrobial agents (e.g., antibacterials, antimycobacterials and antiretrovirals) with different mechanisms of action, as well as to other pathogens (e.g., Staphylococcus aureus, tuberculosis and HIV) with different microbiological characteristics.